Multi-objective route optimization for electric vehicle hazardous materials transportation in uncertain environments

Area of Science:

• Transportation Engineering
• Logistics Management
• Environmental Science

Background:

• Hazardous materials transportation demands high safety and timeliness.
• Electric vehicles (EVs) offer a sustainable alternative for logistics.
• Uncertainty in population density and cargo volume impacts transportation risks and power consumption.

Purpose of the Study:

• To develop a multi-objective path optimization model for Category 9 hazardous materials transportation using electric vehicles.
• To minimize transportation risks and costs while maximizing customer satisfaction.
• To address uncertainties in population density and cargo volume.

Main Methods:

• A multi-objective path optimization model incorporating constraints on accident probability, cargo volume, and time windows was developed.
• An improved Non-dominated Sorting Genetic Algorithm II (NSGA-II), termed H-NSGA-II, was designed by fusing greedy algorithm characteristics with traditional NSGA-II.
• Case validation was performed to assess the algorithm's efficiency in obtaining high-quality Pareto solutions.

Main Results:

• The H-NSGA-II algorithm efficiently generates high-quality Pareto solutions for hazardous materials transportation.
• Compared to the standard NSGA-II, H-NSGA-II achieved significant improvements: 14.40% reduction in transportation risk, 12.81% reduction in transportation cost, and 13.53% increase in average customer satisfaction.
• The model effectively balances competing objectives in complex urban logistics scenarios.

Conclusions:

• The developed H-NSGA-II algorithm provides effective decision-making support for the safe, economical, and green distribution of urban Category 9 hazardous materials.
• This research highlights the potential of electric vehicles in enhancing the sustainability and efficiency of hazardous goods logistics.
• The findings contribute to optimizing urban transportation networks for sensitive cargo under uncertain conditions.